graph neural networks

Reproduction Notebook for the Manuscript: "Causal-Graph-Informed Graph Neural Networks for Climate-Resilience Investment Allocation: A Reproducible Discovery-to-Decision Pipeline"

Reproduction notebook (.ipynb) of the manuscript "Causal-Graph-Informed Graph Neural Networks for Climate-Resilience Investment Allocation: A Reproducible Discovery-to-Decision Pipeline"

Privacy-Preserving Fraud Intelligence for Kenya's Account-to-Account Payment Ecosystem

This whitepaper proposes a privacy-preserving federated graph intelligence architecture for fraud detection across Kenya’s account-to-account payment ecosystem, using PesaLink as the principal infrastructure context. The research addresses a structural limitation of institution-level fraud detection: fraudulent activit...

Confronting the privacy leak epidemic in the machine learning era

As machine learning becomes increasingly integrated into modern digital infrastructure and mobile applications, concerns about user data privacy have grown significantly. Advanced ML models frequently rely on sensitive personal data to deliver intelligent and personalized services. However, this reliance also introduce...

Fault Diagnosis of a Grid-Forming Hybrid Energy Storage Power Station Based on a Physics-Informed Heterogeneous Temporal Graph Neural Network Constrained by Virtual Synchronous Generator Control Equations

Fault diagnosis in grid-connected grid-forming hybrid energy storage station (GFM-HESS) systems is challenging because fault transients are jointly affected by converter control dynamics, multi-source electrical couplings, operating-condition variations, and measurement noise. To address these characteristics, this pap...

Reproduction Notebook for the Manuscript: "Causal-Graph-Informed Graph Neural Networks for Climate-Resilience Investment Allocation: A Reproducible Discovery-to-Decision Pipeline"

Reproduction notebook (.ipynb) of the manuscript "Causal-Graph-Informed Graph Neural Networks for Climate-Resilience Investment Allocation: A Reproducible Discovery-to-Decision Pipeline"

Emerging Trends in Graph-Based Network Science: A Critical Review of Algorithms, Artificial Intelligence and Complex Network Applications

Graph-based network science has evolved from its traditional foundations in discrete mathematics into an interdisciplinary field that integrates graph algorithms, network analysis, machine learning, artificial intelligence, knowledge representation, and temporal modelling. This study critically reviews this evolution,...

Graph neural networks and transformers for antimalarial drug discovery under distribution shift

Description: Complete reproducibility records for “Graph neural networks and transformers for antimalarial drug discovery: honest-negative results underdistribution shift” submitted to the Journal of Computer-Aided Molecular Design (JCAMD). This deposit provides: - Core dataset (19,836 molecules from African natural pr...

Optimizing electric vehicle charging station placement using graph neural networks for grid load balancing

"SIMPLE-ICS Testbed Dataset"

"SIMPLEICS Labeled DatasetA multi-source labeled ICS\/OT security dataset derived from the SIMPLEICS testbed, designed for machine learning (ML), deep learning (DL), hyperdimensional computing (HDC), graph neural network (GNN), and event correlation research.OverviewThis project transforms raw sensor logs from the SIMP...

Agent-Based Simulation and Eleven-Stage ML Pipeline Code for Climate-Adaptive Practice Adoption Among Smallholder Farmers (Zone K)

Quantifying Social Diffusion in Climate-Adaptive Practice Adoption: An Agent-Based Simulation and Explainable Machine Learning Framework for Smallholder Farmers." It contains the agent-based simulation generator and the complete eleven-stage analysis pipeline used to produce every table, figure, and reported statistic...

GRAPH NEURAL NETWORK (GNN) BASED TOPOLOGY CONTROL IN SELF-ORGANIZING WHEELED ROBOT SWARMS

This study examines a Graph Neural Network (GNN)-based approach for controlling communication topology and coordinated motion in self-organizing wheeled robot swarms under dynamically changing spatial and network conditions. The proposed framework represents robots as graph nodes and wireless communication links as gra...

Pformer: a plug-and-play periodicity-aware embedding for crystal property prediction

Predicting macroscopic properties of crystalline materials from atomic structure remains a central challenge in computational materials science. We introduce Pformer, a modular crystal representation framework built around a Fourier-inspired source encoder. The source model is trained by supervised scalar-property re...